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2013 (Vol. 6, Issue: 12)
Article Information:

Econometric and RBF Neural Network Models for Analyzing Automobile Demand in Iran

Hamed Pouralikhani, Alimohammad Kimiagari, Mohsen Keyvanloo and Hesamaddin Najmi
Corresponding Author:  Hamed Pouralikhani 

Key words:  CHAID method, RBF neural network, regression method, RMSE, time series method, ,
Vol. 6 , (12): 2171-2180
Submitted Accepted Published
December 07, 2012 January 11, 2013 July 30, 2013
Abstract:

Identifying factors having influential impact on automobile demand estimation has become a primary concern for automobile industry. There has been substantial development modeling for automobile demand estimation. The Regression, Time series, CHAID and RBF neural network modeling types are proposed. In this study, automobile demand is classified into three classes. The first issue to be addressed by this study is to selecting the most appropriate modeling types in each class by comparing the actual demand and estimated demand in terms of Root-Mean-Square-Error (RMSE). Results indicate that modeling can be different for different classes of automobile demand. The second aim of this study is to identify the significant indicators for estimating automobile demand in each class of Iranian market.
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  Cite this Reference:
Hamed Pouralikhani, Alimohammad Kimiagari, Mohsen Keyvanloo and Hesamaddin Najmi, 2013. Econometric and RBF Neural Network Models for Analyzing Automobile Demand in Iran.  Research Journal of Applied Sciences, Engineering and Technology, 6(12): 2171-2180.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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